Search

To solve the problem of data distribution difference among different corpora, this paper proposed a cross-corpus speech emotion recognition algorithm based on subdomain adaptive deep autoencoder. Firstly, it used two depth autoencoders to obtain representative low-dimensional emotional features of source domain and target domain, respectively. Then, it used an adaptive sub-domain module based on LMMMD to achieve the alignment of feature distribution between source domain and target domain in different low-dimensional emotional category spaces. Finally, it used the tagged source domain data to supervise the training of the model. In the cross-corpus recognition scheme with eNTERFACE library as the source domain and Berlin library as the target domain, the accuracy of the proposed algorithm 5.26%～19.73% higher than that of other algorithms. In the cross-corpus recognition scheme with Berlin library as the source domain and eNTERFACE library as the target domain, the accuracy of the proposed algorithm is 7.34%～8.18% higher than that of other algorithms. Therefore, the proposed method can effectively extract the common sentiment features of different corpora and improve the performance of cross-corpus speech sentiment recognition. [ABSTRACT FROM AUTHOR]